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Bias attenuation results for nondifferentially mismeasured ordinal and coarsened confounders

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  • Elizabeth L. Ogburn
  • Tyler J. Vanderweele

Abstract

Suppose we are interested in the effect of a binary treatment on an outcome where that relationship is confounded by an ordinal confounder. We assume that the true confounder is not observed but, rather, we observe a nondifferentially mismeasured version of it. We show that, under certain monotonicity assumptions about its effect on the treatment and on the outcome, an effect measure controlling for the mismeasured confounder will fall between the corresponding crude and true effect measures. We also present results for coarsened and, under further assumptions, multiple misclassified confounders. Copyright 2013, Oxford University Press.

Suggested Citation

  • Elizabeth L. Ogburn & Tyler J. Vanderweele, 2013. "Bias attenuation results for nondifferentially mismeasured ordinal and coarsened confounders," Biometrika, Biometrika Trust, vol. 100(1), pages 241-248.
    • Handle: RePEc:oup:biomet:v:100:y:2013:i:1:p:241-248
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    File URL: http://hdl.handle.net/10.1093/biomet/ass054
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    Cited by:

    1. Rahul Singh, 2020. "Kernel Methods for Unobserved Confounding: Negative Controls, Proxies, and Instruments," Papers 2012.10315, arXiv.org, revised Mar 2023.
    2. Peña Jose M., 2020. "On the Monotonicity of a Nondifferentially Mismeasured Binary Confounder," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 150-163, January.
    3. Sjölander, Arvid & Peña, Jose M. & Gabriel, Erin E., 2022. "Bias results for nondifferential mismeasurement of a binary confounder," Statistics & Probability Letters, Elsevier, vol. 186(C).
    4. Peña Jose M., 2020. "On the Monotonicity of a Nondifferentially Mismeasured Binary Confounder," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 150-163, January.

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